However, the commonly used IoU metric has two main drawbacks. (1) On one hand, it is difficult to set a proper threshold for IoU during training. A lower IoU threshold (e.g. 0.5) could keep adequate number of positive samples but will result in many "close but not correct" false positives during inference [1]}. A relative higher threshold (e.g. 0.7) can reject low-quality proposals but will reduce a large number of matched positives. Although the Cascade R-CNN [2]} and ALFNet [1]} have provide solutions that gradually refine the proposals by several stages with a set of IoU thresholds, the hand-crafted IoU threshold, whether single or multiple, is still not the best choice.
(2) On the other hand, the IoU-based GT-Proposal assignment suffers from the inconsistent supervision problem that spatially adjacent proposals with similar features are assigned to different ground-truth boxes, which means some very similar proposals may be forced to regress towards different targets, and thus confuses the bounding-box regression when predicting the location results. This disadvantage is more prominent in the scene with large scale variation and occlusion.
As illustrated in Figure REF , overlapped proposals with almost the same features are assigned to different ground-truth boxes, which results in the confusion during training and thus brings about low-quality regression results during inference (Figure REF  red solid box).
